There is a technology that recognizes the category of an object according to the similarity calculated by extracting the feature amount of the object from the image data of the object captured by an image capturing section and comparing the feature amount with the feature amount data registered in the recognition dictionary. Such a technology for recognizing the object included in an image is called as generic object recognition. About the technologies of the generic object recognition, various recognition technologies are described in the following document.
Keiji Yanai “Present situation and future of generic object recognition”, Journal of Information Processing Society, Vol. 48, No. SIG16 [Search on Heisei 22 August 10], Internet <URL: http://mm.cs.uec.ac.jp/IPSJ-TCVIM-Yanai.pdf>
In addition, the technology carrying out the generic object recognition by area division of the image for each object is described in the following document.
Jamie Shotton etc, “Semantic Texton Forests for Image Categorization and Segmentation”, [Search on Heisei 22 August 10], Internet <URL:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.145.3036&rep=repl&type=pdf>
In recent years, for example, the generic object recognition technology is proposed to be applied in a recognition apparatus for recognizing commodity especially the commodity without a barcode such as vegetables, fruits and the like bought by a customer in a checkout system of a retail store. In this condition, the feature amount data for presenting the surface information such as appearance shape, color, pattern, and concave-convex situation of a recognition object commodity by parameters are stored in the recognition dictionary. The commodity recognition apparatus extracts the feature amount of appearance of the commodity from the image data of the commodity captured by the image capturing module and compares the feature amount with the feature amount data of each commodity registered in the recognition dictionary. And then the commodity recognition apparatus outputs the commodity similar in feature amount as the recognition commodity candidate.